class_docstr: Encode the categorical features to numerical features.
  
  # Arguments
      encoding: A list of strings, which has the same number of elements as
          the columns in the structured data. Each of the strings specifies
          the encoding method used for the corresponding column. Use 'int' for
          categorical columns and 'none' for numerical columns.
code: def get_config(self):
      config = {
          "encoding": self.encoding,
      }
      base_config = super().get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
imports: from typing import List
  import tensorflow
  from tensorflow import keras
  from tensorflow import nest
  from tensorflow.keras import layers
  from tensorflow.keras.layers.experimental import preprocessing
  from autokeras.utils import data_utils
label_desc: Use image node_id 5 for calling the MultiCategoryEncoding obj's underlying member method code with example usage: obj.get_config() and returns: dict